Time series processing-based malicious activity detection in SCADA systems

Many critical infrastructures, essential to modern life, such as oil and gas pipeline control and electricity distribution, are managed by SCADA systems. In the contemporary landscape, these systems are interconnected to the internet, rendering them vulnerable to numerous cyber-attacks. Consequently, ensuring SCADA security has become a crucial area of research. This paper focuses on detecting attacks that manipulate the timing of commands within the system, while maintaining their original order and content. To address this challenge, we propose several machine-learning-based methods. The first approach relies on Long-Short-Term Memory model, and the second utilizes Hierarchical Temporal Memory model, both renowned for their effectiveness in detecting patterns in time-series data. We rigorously evaluate our methods using a real-life SCADA system dataset and show that they outperform previous techniques designed to combat such attacks.